So... What IS an AI Agent?
Cutting through the marketing noise to find a real definition of AI agents by examining how OpenAI, Google, Anthropic, and AWS define them — and what they all have in common.
This post was originally published on LinkedIn.
I read the recent TechCrunch article aptly named “No one knows what the hell an AI agent is” and honestly, it got a chuckle out of me. Not because I found the title snappy or glib, but rather extremely accurate; it is my experience chatting with people in the tech industry that this term is nebulous and hard to pinpoint to an exact definition.
So, in this article I aim to clarify the concept by examining how leading companies define AI agents and identifying the common core elements that unite these definitions.
Why definition clarity matters
Think about it briefly. How can we benchmark performance across different agent implementations, when the goalpost of what an agent is — the success criteria of a product — is not a common standard?
Worse, the potential “misaligned expectations” about agent capabilities. If the expectations from a client are to receive something with a certain degree of autonomy in decision making, but they receive a static LLM-powered workflow, this could lead to serious business consequences. Consider a company investing millions in what they believe is an autonomous agent system, only to discover they’ve purchased what amounts to a sophisticated chatbot.
Similarly, how then can we effectively measure return on investment from agent-based projects? If there is no standard on what an agent can do, deciding a measure becomes an arbitrary exercise. An organization might claim significant ROI from their “agent” implementation while actually measuring metrics that don’t capture true agentic capabilities at all.
In short, if there’s no single agreed definition, when two people are talking about agents, what they can accomplish and the kind of results they expect to get, they might be talking about wildly different things.
This definitional gap doesn’t just create academic confusion — it creates real business risk in a market expected to grow exponentially over the coming years.
The origins of confusion
The confusion surrounding AI agents stems from several factors.
“The concepts of AI ‘agents’ and ‘agentic’ workflows used to have a technical meaning,” Ng said in a recent interview, “but about a year ago, marketers and a few big companies got a hold of them.”
Evolution from technical to marketing term
This is often the case with emerging tech. A term becomes a buzz word and all marketing teams rush to plug it anywhere to ride the hype, diluting the definition and adding to the cacophony. Even within companies in the industry there is inconsistency in the way that these are used, for example where terms like “assistant” and “agent” are sometimes used interchangeably.
Rapid technology evolution
AI agents are constantly evolving as companies release new capabilities. OpenAI, Google, and Perplexity have recently launched their first agent products — each with different capabilities and approaches. Even their definitions of what is an agent are significantly different, each focusing on a facet of it.
Varied implementation emphasis
Companies highlight different aspects of agents based on their specific implementations and market positioning. This leads to definitions that emphasize different characteristics while describing essentially similar concepts.
Without a common framework, organizations struggle to assess AI agent capabilities and determine their practical value.
How leading companies define AI agents
Taking a closer look at the definitions that some of the main players in the AI space use, we can see some patterns — different aspects that for the reasons above are not quite aligned, but rather highlight a different aspect of a multifaceted concept:
OpenAI’s definition
Systems that intelligently accomplish tasks, ranging from executing simple workflows to pursuing complex, open-ended objectives
OpenAI defines agents in their own documentation.
Their approach emphasizes a component-based framework with five essential domains:
- Models: Core intelligence for reasoning
- Tools: Interfaces to interact with the environment
- Knowledge & Memory: External knowledge systems
- Guardrails: Systems to prevent harmful behavior
- Orchestration: Frameworks to develop and improve agents
Google’s definition
An autonomous system that can observe its environment, reason about its goals, and take actions using external tools to achieve a desired outcome.
In their technical whitepaper, Google provides this definition. Google’s leaders also described an agent in the most semantically literal definition as “someone or something that acts on your behalf.”
They emphasize two key elements:
- It’s empowered to act on your behalf. You give it permission and authority
- It is autonomous and able to take action using external tools to achieve an outcome
Anthropic’s definition
Systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.
Anthropic provides a process-oriented definition.
They also make a very important distinction:
- Workflows are systems where LLMs and tools are orchestrated through predefined code paths.
- Agents, on the other hand, are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.
This is extremely important to grasp, as there are many LLM-enhanced workflows that can take actions based on LLM output, but stretch the definition of what an agent is to a point that makes it indistinguishable from older NLP processes.
AWS’s definition
An artificial intelligence (AI) agent is a software program that can interact with its environment, collect data, and use the data to perform self-determined tasks to meet predetermined goals.
AWS offers a concise definition in their documentation.
Bringing it all together
Despite their different emphases, these definitions share several fundamental elements:
- Autonomous action: The ability to operate with reduced human supervision
- Goal-oriented behavior: Working toward specified objectives
- Environmental interaction: Perceiving and affecting the surrounding world
- Decision-making capability: Using reasoning to determine appropriate actions
- Tool utilization: Leveraging external capabilities to extend functionality
A synthesized understanding
Based on these common elements, we can then come to a synthesized definition:
“An AI agent is a system that can autonomously perceive information, make decisions through reasoning, and take actions to accomplish specified goals, often through the use of external tools and with the ability to adapt to changing circumstances.”
This definition captures the essential characteristics while avoiding overly restrictive technical specifications or overly broad marketing language. It is fluff-less like AWS’s own definition, but captures some of the nuance that Anthropic so effectively delineated by differentiating Agents from LLM-powered Workflows.
To wrap up
The confusion about AI agents stems not from fundamental disagreements about what they are, but from different emphases on aspects of the same core concept. By understanding the common elements across definitions — autonomy, goal-orientation, environmental interaction, and decision-making capability — we can navigate the varied terminology and focus on the practical value these systems provide.
As AI agent technology continues to evolve, the definitions will likely become more standardized. Until then, focusing on the fundamental capabilities rather than marketing terminology will help businesses and developers make informed decisions about how to leverage this transformative technology.